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Related Experiment Videos

Enhanced intrusion detection in cybersecurity through dimensionality reduction and explainable artificial

Hayam Alamro1, Sultan Alahmari2, Nadhem Nemri3

  • 1Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia.

Scientific Reports
|September 30, 2025
PubMed
Summary

This study introduces an Enhanced Intrusion Detection in Cybersecurity through Dimensionality Reduction and Explainable Artificial Intelligence with Attention Mechanism in Deep Learning (EIDCDR-XAIADL) model. The novel approach significantly improves cybersecurity threat detection accuracy using explainable AI and deep learning techniques.

Keywords:
Antlion optimizationCybersecurityDeep learningExplainable artificial intelligenceIntrusion detection system

Related Experiment Videos

Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Machine Learning

Background:

  • Cyber threats are increasingly complex, utilizing evasion techniques to bypass Intrusion Detection Systems (IDSs).
  • Artificial Intelligence (AI) in cybersecurity lacks transparency and interpretability, hindering trust and error identification.
  • Explainable AI (XAI) offers a solution by making AI decisions understandable for cybersecurity experts.

Purpose of the Study:

  • To propose an Enhanced Intrusion Detection in Cybersecurity through Dimensionality Reduction and Explainable Artificial Intelligence with Attention Mechanism in Deep Learning (EIDCDR-XAIADL) model.
  • To develop a robust cybersecurity system that integrates XAI for improved threat detection and decision-making.
  • To address the challenge of recognizing obfuscated malware and enhancing the transparency of AI-driven security.

Main Methods:

  • Data normalization using mean normalization and feature selection via Multiverse Optimization (MVO).
  • Hybrid deep learning model combining Convolutional Neural Network (CNN), Bi-directional Gated Recurrent Unit (BiGRU), and Attention Mechanism (CNN-BiGRU-AM) for attack classification.
  • Hyperparameter optimization using Antlion Optimization (ALO) and explainability provided by Shapley Additive Explanations (SHAP).

Main Results:

  • The EIDCDR-XAIADL model achieved superior accuracy rates of 99.19% on the NSLKDD dataset and 99.12% on the CICIDS 2017 dataset.
  • The integration of XAI (SHAP) provided trustworthy insights, enhancing threat detection and expert decision-making.
  • Dimensionality reduction and optimized deep learning models contributed to effective intrusion detection.

Conclusions:

  • The proposed EIDCDR-XAIADL model demonstrates significant effectiveness in enhancing cybersecurity intrusion detection.
  • Explainable AI integration is crucial for building trust and understanding in AI-powered cybersecurity solutions.
  • The study highlights the potential of combining advanced machine learning, dimensionality reduction, and XAI for robust cyber defense.